Natural Language Processing (Almost) from Scratch
Abstract
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.
Cite
Text
Collobert et al. "Natural Language Processing (Almost) from Scratch." Journal of Machine Learning Research, 2011.Markdown
[Collobert et al. "Natural Language Processing (Almost) from Scratch." Journal of Machine Learning Research, 2011.](https://mlanthology.org/jmlr/2011/collobert2011jmlr-natural/)BibTeX
@article{collobert2011jmlr-natural,
title = {{Natural Language Processing (Almost) from Scratch}},
author = {Collobert, Ronan and Weston, Jason and Bottou, Léon and Karlen, Michael and Kavukcuoglu, Koray and Kuksa, Pavel},
journal = {Journal of Machine Learning Research},
year = {2011},
pages = {2493-2537},
volume = {12},
url = {https://mlanthology.org/jmlr/2011/collobert2011jmlr-natural/}
}